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Today we’re pleased to announce that our article “The Geography of Happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place” has been officially published by PLoS ONE. We wanted to tell you about one key piece we’ve added to the paper and an unusual new Twitter account we’ve created.

After our threeblogposts (which coincided with the release of the preprint), we received plentyofmediaattention, as well as some fantastic feedback from readers (thanks!). One very important question kept coming up: “How well does happiness agree with other measures of well-being?”, or more simply: “Why should we believe you?”

Well, we’re glad you asked. For the final paper, we’ve added a US state-level comparison between our happiness measure and five other kinds of well-being indices:

the Behavioral Risk Factor Surveillance Survey (BRFSS) for which people were asked to rate their life satisfaction on a scale of 1 to 4 (the BRFSS was explored in this Science paper on well-being from a few years back);

gun violence, specifically the number of shootings per 100,000 people.

In the figure below, we show a series of scatter plots comparing all pairs of well-being metrics (happiness runs along the top row). Each dot represents a US state, and the colors represent strength of correlation or agreement between measures, with blue meaning strong agreement, and red representing no (statistically significant) agreement. (We include the exact Spearman correlation coefficient r and p-value in each scatter plot.)

Scatter matrix showing comparison between different well-being metrics for all US states. The top row shows comparisons with happiness. Colors indicate the strength of correlation between pairs of metrics; shades of blue indicate increasingly significant correlation.

Looking at the top row, we can immediately see that happiness agrees with all measures except for the BRFSS. However, the BRFSS itself doesn’t agree with any other measure except for the Gallup well-being index. The most striking departure was the BRFSS ranking Louisiana as the happiest state whereas our happiness measure placed it last. There are a number of possible explanations for these disagreements: one is that the BRFSS data was taken between 2005 and 2008, while all other data is from 2011 only; another is that unlike the other measures, happiness is self-reported in the BRFSS. How would you answer if asked how happy you are? Do you expect that your answer is representative of the population you live in at large? There are certainly many different ways to define “happiness”, as a number of different readers have pointed out.

Of course, this is not to criticize the BRFSS (it remains a significant data source, and Oswald & Wu did fine work analyzing it in their Science paper), but merely to suggest that our word happiness score is measuring something different but perhaps complementary to traditional survey-based techniques. There certainly appears to be plenty of value to observing people “in the wild” via social network data, e.g. with the real-time instrument hedonometer.org.

Finally, to celebrate the publication of our article we created a Twitter feed, @geographyofhapp, dedicated to tweeting the happiest and saddest city every day, and we invite you to follow. We’re hoping that this is the first research article with its own Twitter account, but perhaps not hoping that it represents the future of scientific publishing…

Is Disneyland really the happiest place on Earth?* How happy is the city you live in? We have already seen how the hedonometer can be used to find the happiest street corner in New York City, now it’s time to let it loose on the entire United States.

We plotted over 10 million geotagged tweets from 2011 (all our results are in this paper, also on the arxiv), coloring each point by the average happiness of nearby words (detail on how we calculate happiness can be found in this article published in PLoS ONE):

As well as cities and the roads between them, we can make out many regions of higher and lower happiness, even within individual cities. As an example, check out this tweet-generated map of the city of Chicago:

Tweet-generated map of Chicago. Click to enlarge.

Notice the striking contrast between the relatively happy Central/North Side of the city, and the sadder South Side. You can also find a few airports in this map, and if you look very closely you might even be able to pick out happy and sad terminals!

To quantify this variation in happiness a bit better, let’s look at the average happiness of each state:

Southern states tend to produce sadder words than those in northern New England or out west. Hawaii emerges as the happiest state and Louisiana as the saddest, due to relative differences in the frequencies of happy and sad words used in each state. Here at onehappybird, we characterize such differences by “word shifts”, which are basically word clouds for grown-ups. You can find examples of these, as well as the full list of the average happiness of each state, here (page best viewed using Google Chrome).

Zooming in further to the level of cities, we produced a similar list for 373 cities in the lower 48 states (you can find the full list, as well as maps and word shifts for each city, here). With a score of 6.25, we found the happiest city to be Napa, CA, due to a relative abundance of such happy words as “restaurant”, “wine”, and even “cheers”, along with a lack of profanity.

At the other end of the spectrum, we found the saddest city to be Beaumont, TX, with a score of 5.82. In general, cities in the south tended to be less happy than those in the north, with a major contributing factor being the relative abundance of profanity used in those cities.

We can go even further than this, and group cities by similarities in word usage. Each square in the heatmap below represents the similarity (Spearman correlation for you mathematically minded onehappybird watchers) between word distributions for the largest cities in the US. Red squares mean that the corresponding cities use words in a similar fashion, while blue means that those cities tend to use different types of words with respect to each other. The colors in the tree diagram at the top signify clusters of cities exhibiting similar word usage (below a certain threshold).

As we might expect for two cities that are geographically nearby, New Orleans and Baton Rouge are clumped together at the bottom right of the figure. On the other hand, New York and Seattle get clumped together as well, suggesting that similarities in language depend on more than just geographical proximity.

You can find more information about happiness and cities, as well as details on the methods used to produce these results, in our arxiv research article. In our next post, we’ll look at how these results are related to various underlying socioeconomic characteristics of cities. What makes a city happy or sad? Can we use Big Data to predict future changes in the demographics, health, or happiness of a city? How does happiness relate to the food you eat?

*By the way, to answer the question at the start of this post: According to this analysis Disneyland is not the happiest place on Earth; it isn’t even the happiest place in Southern California! See if you can find it in this tweet-generated map of LA! Or find your city here.